Richard D. Boyce, PhD is an Assistant Professor of Biomedical Informatics at the University of Pittsburgh. He has training in informatics, pharmacoepidemiology, and comparative effectiveness research as well as a strong commitment to improving medication safety for older adults. His prior work has focused on artificial intelligence methods for predicting drug-drug interactions and the comparative safety of antidepressants for treating de-pressed nursing home residents. The current proposal is for a three year Mentored Research Scientist Development Award (K01) from the National Institute on Aging for training and support that will address gaps in his knowledge of aging research and the nursing home setting. In collaboration with his mentoring team (Drs. Charles Reynolds, Jordan Karp, and Steven Handler) he has developed a training and research plan that will both fill in these knowledge gaps and prepare him for an independent research career. The overarching goal of his K01 is to become an expert on how to effectively translate research results from pharmacoepidemiology and comparative effectiveness research to informatics interventions that improve medication safety for older adults, especially nursing home residents. To accomplish this goal, he proposes eight career development activities and two research aims that will help him 1) obtain rigorous training in the science and practice of creating clinical decision support interventions that are effective in the nursing home setting; 2) obtain a thorough understanding of how falls and fall risk factors are detected, monitored, reported, and assessed in the nursing home setting; and 3) learn the perceptions of multiple nursing home clinician stakeholders on what research is needed to help reduce medication-related adverse events. The two research aims will enable him to integrate and apply the knowledge that he will gain through the proposed training activities by exploring the feasibility and potential clinical usefulness of actively monitoring patients exposed to psycho- tropic PDDIs. The approach would use electronic data available in most United States (US) NHs to provide highly specific and actionable alerts to physicians and/or nurses when a resident who is exposed to a PDDI involving a psychotropic drug transitions to a state of unacceptably high risk for experiencing a fall. Dr. Boyce's hypothesis is that this approach will extend the ability of NH clinicians to perform the recommended diligent monitoring of PDDIs, while avoiding alert fatigue. He anticipates that the approach would be generalizable to other prevalent NH ADEs (e.g., delirium) and other drug classes (e.g., mood stabilizers). This proposal is relevant to public health because it will explore an innovative approach to managing PDDIs that may ultimately reduce the occurrence of one of the most prevalent multifactorial geriatric syndromes (falls). It wil also inform an R-series grant proposal to study a mature intervention that Dr. Boyce plans to submit in year 2 of the project.